Data Mining Metrics


Data Mining Metrics-

In data mining data coming from lots of centralized database and operational database, Data mining metrics works for data and algorithm selection mainly. that all data coming with noisy and incompleteness. Data can be selected for cleaning before giving to data mining can apply a different algorithms on that data and create a great pattern for that data.

IT is used for measurement and comparison of given result.That are used for ensure that the data mining task is working good-because of data mining patterns are most important for user for giving future decision. matrices apply rules and regulations before creating patterns in data mining. It applies lots of techniques and rules and regulations on data before data mining pattern creation.metrics is collection of set of measurements.

In data mining two types of methods are used-

  1. Descriptive method

    2. Predictive method

Data coming from data warehouse and data mining apply classification, clustering, prediction, time series analysis, Association rule, regression algorithms. It helps to data mining for selecting a good algorithm for or data, after applying algorithm that observe the data mining patterns. DM metrics complete assessment of data that are more important for final result.

Data mining metrics are mainly work on data accuracy and reliability and usefulness. Accuracy is measured after how many percent that pattern are useful to user. accuracy is depend upon usefulness of that pattern to user and which algorithm are apply to that data.

It is most important part in data mining for better output and better pattern to giving future decisions

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